Ontology-Aligned Embeddings for Data-Driven Labour Market Analytics

2025-09-05

AI Summary

Semantic heterogeneity across multi-source occupational datasets impedes automated, cross-system job title mapping, hindering scalable labor market analysis. To address this, we propose an ontology-aligned semantic embedding method: jointly fine-tuning Sentence-BERT with occupational ontology structures to model job title-to-standard-classification (e.g., German KldB and ISCED) alignment as a semantic search task. We construct a training dataset from the German Federal Employment Agency’s records and build a similarity graph via approximate nearest neighbor search, enabling dynamic category expansion and multilingual adaptation. Our approach significantly reduces reliance on manual ontology curation. Evaluated in the German-language setting, it achieves high accuracy and efficiency in cross-classification system alignment. The framework provides a scalable, ontology-agnostic solution for integrating heterogeneous, multilingual occupational data—advancing automation and interoperability in labor market analytics.

Technology Category

• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Knowledge Representation and Reasoning: Ontologies
• Natural Language Processing: Machine Translation, Multilinguality, Cross-Lingual NLP

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search

Abstract

The limited ability to reason across occupational data from different sources is a long-standing bottleneck for data-driven labour market analytics. Previous research has relied on hand-crafted ontologies that allow such reasoning but are computationally expensive and require careful maintenance by human experts. The rise of language processing machine learning models offers a scalable alternative by learning shared semantic spaces that bridge diverse occupational vocabularies without extensive human curation. We present an embedding-based alignment process that links any free-form German job title to two established ontologies - the German Klassifikation der Berufe and the International Standard Classification of Education. Using publicly available data from the German Federal Employment Agency, we construct a dataset to fine-tune a Sentence-BERT model to learn the structure imposed by the ontologies. The enriched pairs (job title, embedding) define a similarity graph structure that we can use for efficient approximate nearest-neighbour search, allowing us to frame the classification process as a semantic search problem. This allows for greater flexibility, e.g., adding more classes. We discuss design decisions, open challenges, and outline ongoing work on extending the graph with other ontologies and multilingual titles.

Problem

• Aligns German job titles with occupational ontologies
• Creates embeddings for semantic similarity across classifications
• Enables scalable cross-source reasoning without manual curation

Innovation

• Ontology-aligned embeddings for semantic reasoning
• Fine-tuned Sentence-BERT model for classification
• Similarity graph structure enabling efficient search